Test difference between means of entire collection (EC) and core set (CS) for quantitative traits by Student's t test (in particular it's adaptation for comparison of groups with unequal variances known as Welch's two sample t-test) (Student 1908; Welch 1947) .
Arguments
- data
The data as a data frame object. The data frame should possess one row per individual and columns with the individual names and multiple trait/character data.
- names
Name of column with the individual names as a character string.
- quantitative
Name of columns with the quantitative traits as a character vector.
- selected
Character vector with the names of individuals selected in core collection and present in the
namescolumn.
Value
- Trait
The quantitative trait.
- Count
The accession count (excluding missing data).
- Df
The degrees of freedom for the test.
- EC_Min
The minimum value of the trait in EC.
- EC_Max
The maximum value of the trait in EC.
- EC_Mean
The mean value of the trait in EC.
- EC_SE
The standard error of the trait in EC.
- CS_Min
The minimum value of the trait in CS.
- CS_Max
The maximum value of the trait in CS.
- CS_Mean
The mean value of the trait in CS.
- CS_SE
The standard error of the trait in CS.
- ttest_pvalue
The p value of the Student's t test for equality of means of EC and CS.
- ttest_significance
The significance of the Student's t test for equality of means of EC and CS.
References
Student (1908).
“The probable error of a mean.”
Biometrika, 6(1), 1–25.
Welch BL (1947).
“The generalization of 'student's' problem when several different population varlances are involved.”
Biometrika, 34(1-2), 28–35.
Examples
data("cassava_CC")
data("cassava_EC")
ec <- cbind(genotypes = rownames(cassava_EC), cassava_EC)
ec$genotypes <- as.character(ec$genotypes)
rownames(ec) <- NULL
core <- rownames(cassava_CC)
quant <- c("NMSR", "TTRN", "TFWSR", "TTRW", "TFWSS", "TTSW", "TTPW", "AVPW",
"ARSR", "SRDM")
qual <- c("CUAL", "LNGS", "PTLC", "DSTA", "LFRT", "LBTEF", "CBTR", "NMLB",
"ANGB", "CUAL9M", "LVC9M", "TNPR9M", "PL9M", "STRP", "STRC",
"PSTR")
ec[, qual] <- lapply(ec[, qual],
function(x) factor(as.factor(x)))
ttest.evaluate.core(data = ec, names = "genotypes",
quantitative = quant, selected = core)
#> Trait Count Df EC_Min EC_Max EC_Mean EC_SE CS_Min CS_Max
#> df...1 NMSR 1684 197.5347 1.00 55.00 11.722090 0.18731651 1.00 55.00
#> df...2 TTRN 1684 191.2705 0.25 13.75 3.853909 0.04656232 0.25 13.75
#> df...3 TFWSR 1684 185.9321 0.00 40.00 5.428979 0.11045567 0.20 38.00
#> df...4 TTRW 1684 178.3429 0.00 20.20 1.897948 0.04037796 0.10 20.20
#> df...5 TFWSS 1684 189.6987 0.20 42.00 6.943052 0.14359496 0.20 42.00
#> df...6 TTSW 1684 181.2810 0.04 22.00 2.387502 0.04878843 0.10 22.00
#> df...7 TTPW 1684 187.6470 0.40 80.00 12.372031 0.23851356 0.40 80.00
#> df...8 AVPW 1684 179.8673 0.20 33.00 4.285450 0.08195995 0.20 33.00
#> df...9 ARSR 1684 213.3477 0.00 18.00 1.858076 0.05493488 0.00 8.00
#> df...10 SRDM 1684 216.2246 0.50 48.90 37.771021 0.12264395 21.90 48.10
#> CS_Mean CS_SE ttest_pvalue ttest_significance
#> df...1 10.892857 0.6314310 0.209505833 ns
#> df...2 3.930655 0.1754066 0.672855738 ns
#> df...3 6.348214 0.4696134 0.058266745 ns
#> df...4 2.617817 0.2207393 0.001585008 **
#> df...5 7.748214 0.5588314 0.164507433 ns
#> df...6 3.069087 0.2381436 0.005599998 **
#> df...7 14.096429 0.9720528 0.086559633 ns
#> df...8 5.686905 0.4210897 0.001302429 **
#> df...9 1.702381 0.1516377 0.335457319 ns
#> df...10 37.730357 0.3289992 0.907907091 ns